The Reduced Uterine Perfusion Pressure (RUPP) Model of Preeclampsia Causes Decreased Capillary Perfusion in Skeletal Muscle
Bibliographic record
Abstract
The Reduced Uterine Perfusion Pressure (RUPP) model of preeclampsia exhibits angiogenic imbalance, endothelial dysfunction, increased vascular resistance and hypertension. We hypothesized the RUPP model would also exhibit decreased functional capillary density and perfusion in skeletal muscle. Female Sprague‐Dawley rats (n=30) were randomized to nonpregnancy (NP) or breeding (Preg) at 12 weeks of age and again to RUPP or SHAM surgeries on gestational day (GD) 14 (or equivalent age in NP rats). On GD 20 (or equivalent), capillary structure and perfusion of the extensor digitorum longus was imaged using digital intravital video microscopy. Functional videos were analyzed by a blinded observer. Capillary density (CD) was expressed as capillaries/millimeter intersecting 3 staggered reference lines (150µm). Flow was scored as the percentage of capillaries having: 1) continuous, 2) intermittent, or 3) stopped flow. Total CD was not different between groups. The RUPP model decreased continuous flow vessels (main effect of surgery, P<0.01) and increased stopped flow (main effect, p<0.01) both which were more pronounced in Preg animals (Continuous: Preg‐SHAM = 80.1±7.8% vs Preg‐RUPP = 67.8±11.2%, p<0.05) (Stopped: Preg‐SHAM = 8.7±3.2% vs Preg‐RUPP = 17.9±5.7% p<0.01). Our results demonstrate that the RUPP model of preeclampsia (and not just general hypoperfusion) is associated with a decreased capillary perfusion in skeletal muscle. Supported by AIHS, Olympus and Q‐Imaging
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".